Hybrid machine learning techniques for knowledge extraction in communication networks and advanced decision based complex q-Rung orthopair fuzzy frameworks.

Hybrid machine learning techniques for knowledge extraction in communication networks combine the strengths of multiple learning algorithms to analyze complex, high-dimensional, and dynamic network data effectively. Some advanced approaches are discussed to improve the intelligence and efficiency of modern communication networks, making them more resilient and capable of meeting the growing demands of data-driven applications. This article articulates the efficiency and feasibility of a complex q-rung orthopair fuzzy set (Cq-ROFS), which is an extended version of an intuitionistic and q-rung orthopair fuzzy model. The Cq-ROFS has extensive information about any object in the form of amplitude and phase terms. Besides the theoretical concepts of Cq-ROFS, we modified the family of weighted average and weighted geometric operators using fundamental operations of Sugeno-Weber t-norms and t-conorms. An optimization technique of the WASPAS method is adopted to integrate the ranking of alternatives based on multi-criteria decision-making (MCDM) problems. To show the validation and compatibility of diagnosed approaches, we also discussed an experimental case study to assess some advanced communication techniques using developed mathematical approaches and a decision analysis system. The sensitivity analysis and comparative study with existing mathematical methodologies are employed to demonstrate the flexibility and superiority of the discussed approaches.